Most of the works dealing with the detection of FDIAs ignore the spatial correlation of the power grid data, therefore, they fail to fully model the underlying graph topology of the power grid.
There is only a few works [1]}, [2]} in the literature that exploit the spatial correlations of the power grids to detect cyberattacks using Graph Signal Processing (GSP).
Although GSP offers highly efficient tools for cyberattack detection, the custom design steps of spectral filters and scalability problems restrict its usability in real life scenarios.
As a data-driven version of GSP, Graph Convolutional Networks (GCN) predict the filter weights in their hidden layers and eliminate the manual filter design steps.
This automation makes GCNs more attractive to power grid applications, for instance, GCNs are utilized for optimal power flow applications of power grids in [3]}, for cyberattack detection in [4]}, and for cyberattack localization in [5]}.
